CN116385268A - Remote sensing image enhancement method - Google Patents

Remote sensing image enhancement method Download PDF

Info

Publication number
CN116385268A
CN116385268A CN202310365917.0A CN202310365917A CN116385268A CN 116385268 A CN116385268 A CN 116385268A CN 202310365917 A CN202310365917 A CN 202310365917A CN 116385268 A CN116385268 A CN 116385268A
Authority
CN
China
Prior art keywords
resolution image
pixel
image
low
feature
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310365917.0A
Other languages
Chinese (zh)
Inventor
杜磊磊
张丽娜
王伟
魏坤
张伟娜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fengze Smart Technology Co ltd
Original Assignee
Fengze Smart Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fengze Smart Technology Co ltd filed Critical Fengze Smart Technology Co ltd
Priority to CN202310365917.0A priority Critical patent/CN116385268A/en
Publication of CN116385268A publication Critical patent/CN116385268A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformations in the plane of the image
    • G06T3/40Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4053Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
    • G06T3/4076Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution using the original low-resolution images to iteratively correct the high-resolution images
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/70Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/7715Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Multimedia (AREA)
  • Facsimile Image Signal Circuits (AREA)
  • Image Processing (AREA)

Abstract

The invention discloses a remote sensing image enhancement method, which comprises the following steps: A. synchronously shooting a high-resolution image and a second-resolution image of a remote sensing object; B. comparing the high-resolution image with the low-resolution image, and screening the characteristic region; C. extracting a feature pixel set from a feature region marked in the low resolution image; D. deleting the feature pixel set extracted in the step C from the high-resolution image, and then extracting the rest feature pixel sets; E. and C, carrying out enhancement processing on the feature pixel set extracted in the step C and the step D, replacing a corresponding pixel set in the high-resolution image with the feature pixel set subjected to the enhancement processing, and then carrying out anti-logarithmic transformation on the high-resolution image. The invention can improve the defects of the prior art, simplify the enhancement processing steps and improve the image processing efficiency.

Description

Remote sensing image enhancement method
Technical Field
The invention relates to the technical field of image processing, in particular to a remote sensing image enhancement method.
Background
Remote sensing monitoring is a common means of detecting near the ground in a large range. After the original remote sensing image is obtained, the remote sensing image needs to be enhanced so as to improve the accuracy of the subsequent remote sensing image analysis. The existing remote sensing image enhancement means are complex in steps, large in operation amount and low in image processing efficiency.
Disclosure of Invention
The invention aims to provide a remote sensing image enhancement method which can solve the defects of the prior art, simplify enhancement processing steps and improve image processing efficiency.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A remote sensing image enhancement method comprising the steps of:
A. synchronously shooting a high-resolution image and a second-resolution image of a remote sensing object;
B. comparing the high-resolution image with the low-resolution image, and screening the characteristic region;
C. extracting a feature pixel set from a feature region marked in the low resolution image;
D. deleting the feature pixel set extracted in the step C from the high-resolution image, and then extracting the rest feature pixel sets;
E. and C, carrying out enhancement processing on the feature pixel set extracted in the step C and the step D, replacing a corresponding pixel set in the high-resolution image with the feature pixel set subjected to the enhancement processing, and then carrying out anti-logarithmic transformation on the high-resolution image.
Preferably, in step B, screening the feature region includes the steps of,
b1, respectively carrying out logarithmic transformation on the high-resolution image and the low-resolution image;
b2, respectively carrying out high-pass filtering on the transformed high-resolution image and the transformed low-resolution image to obtain a high-frequency image, and respectively carrying out low-pass filtering on the transformed high-resolution image and the transformed low-resolution image to obtain a low-frequency image;
and B3, traversing the high-frequency image and the low-frequency image with high resolution, the high-frequency image and the low-frequency image with low resolution respectively, marking the areas with gray scale change ranges smaller than a set threshold value in each image, comparing the marked areas in the four images, deleting the marked areas in the four images in the high-resolution image and the low-resolution image, and taking the rest areas as characteristic areas.
Preferably, in step C, extracting the set of feature pixels in the marked feature region in the low resolution image comprises the steps of,
c1, gradually expanding the identification radius outwards by taking any gray extreme point in the characteristic region as a central dot;
and C2, after the radius is sequentially identified every time expansion, if no characteristic pixel exists in the radius identification range, calculating the deviation of the included angle between the Euler direction of the newly increased pixel point and the center dot in the radius identification range and the gray gradient direction of the newly increased pixel point and the deviation of the gray gradient amplitude of the newly increased pixel point and the gray gradient difference of the newly increased pixel point and the center origin, judging the pixel point as the characteristic pixel if the calculated included angle and the gray gradient deviation are smaller than a set threshold value, and if the characteristic pixel exists in the radius identification range, calculating the included angle between the Euler direction of the newly increased pixel point and the gray gradient direction of the newly increased pixel point in the radius identification range and the gray gradient amplitude of the newly increased pixel point and the gray gradient difference of the newly increased pixel point and the pixel point closest to the Euler distance, and judging the pixel point as the characteristic pixel if the calculated included angle and the gray gradient deviation are smaller than the set threshold value.
Preferably, in step D, extracting the set of feature pixels in the marked feature region in the high resolution image comprises the steps of,
d1, arbitrarily selecting a plurality of pixel points in the characteristic region as starting points;
d2, marking the pixel points adjacent to the starting point along the gray gradient direction of the starting point from the starting point, marking the pixel points adjacent to the starting point along the gray gradient direction of the newly marked pixel points until the pixel points to be marked are marked, ending the secondary marking process, and deleting other pixel points in a closed area surrounded by the marked pixel points;
and D3, repeating the step D2 until all the rest pixel points in the characteristic region are marked, wherein the rest pixel points are characteristic pixels.
Preferably, in step E, the enhancement processing is performed on the feature pixel set using a gamma correction method.
The beneficial effects brought by adopting the technical scheme are as follows: the invention abandons the technical route of directly enhancing the remote sensing image in the prior art, and utilizes the high-low resolution image to firstly perform comparison and extraction of the characteristic pixel set, so that only the extracted characteristic pixel set is enhanced, the image processing effect is ensured, and the overall operation amount is effectively reduced. And the sensitivity of the high-low frequency image to gray level change is utilized, the characteristic region is marked firstly, and the range of extracting the characteristic pixel set is further narrowed. Because the volume of the low-resolution image is smaller and the image precision is not high, a direct calculation mode is adopted to extract the characteristic pixel set; because the volume of the high-resolution image is larger and the image precision is high, the characteristic pixel set is extracted by adopting an iterative elimination mode. Thereby achieving the purpose of improving the image processing efficiency.
Drawings
FIG. 1 is a flow chart of one embodiment of the present invention.
Detailed Description
Referring to fig. 1, one embodiment of the present invention includes the steps of:
A. synchronously shooting a high-resolution image and a second-resolution image of a remote sensing object;
B. comparing the high-resolution image with the low-resolution image, and screening the characteristic region;
screening the feature region includes the steps of,
b1, respectively carrying out logarithmic transformation on the high-resolution image and the low-resolution image;
b2, respectively carrying out high-pass filtering on the transformed high-resolution image and the transformed low-resolution image to obtain a high-frequency image, and respectively carrying out low-pass filtering on the transformed high-resolution image and the transformed low-resolution image to obtain a low-frequency image;
b3, traversing the high-frequency image and the low-frequency image with high resolution, the high-frequency image and the low-frequency image with low resolution respectively, marking the areas with gray scale change ranges smaller than a set threshold value in each image, comparing the marked areas in the four images, deleting the marked areas in the four images in the high-resolution image and the low-resolution image, and taking the rest areas as characteristic areas;
C. extracting a feature pixel set from a feature region marked in the low resolution image; comprises the steps of,
c1, gradually expanding the identification radius outwards by taking any gray extreme point in the characteristic region as a central dot;
c2, after sequentially identifying the radius by expansion, if no characteristic pixel exists in the radius identification range, calculating the deviation of the included angle between the Euler direction of the newly added pixel point and the center dot in the radius identification range and the gray gradient direction of the newly added pixel point and the deviation of the gray gradient amplitude of the newly added pixel point and the gray gradient difference of the newly added pixel point and the center origin, judging the pixel point as a characteristic pixel if the calculated included angle and gray gradient deviation are smaller than a set threshold value, and if the characteristic pixel exists in the radius identification range, calculating the deviation of the included angle between the Euler direction of the newly added pixel point and the gray gradient direction of the newly added pixel point in the radius identification range and the gray gradient amplitude of the newly added pixel point and the gray gradient difference of the newly added pixel point and the gray gradient direction of the newly added pixel point, and judging the pixel point as the characteristic pixel if the calculated included angle and gray gradient deviation are smaller than the set threshold value;
D. deleting the feature pixel set extracted in the step C from the high-resolution image, and then extracting the rest feature pixel sets; the method comprises the following steps of,
d1, arbitrarily selecting a plurality of pixel points in the characteristic region as starting points;
d2, marking the pixel points adjacent to the starting point along the gray gradient direction of the starting point from the starting point, marking the pixel points adjacent to the starting point along the gray gradient direction of the newly marked pixel points until the pixel points to be marked are marked, ending the secondary marking process, and deleting other pixel points in a closed area surrounded by the marked pixel points;
d3, repeating the step D2 until all the rest pixel points in the characteristic region are marked, wherein the rest pixel points are characteristic pixels;
E. and C, enhancing the feature pixel set extracted in the step C and the step D by using a gamma correction method, replacing the corresponding pixel set in the high-resolution image by using the feature pixel set after enhancing, and performing anti-logarithmic transformation on the high-resolution image.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present invention, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. The remote sensing image enhancement method is characterized by comprising the following steps of:
A. synchronously shooting a high-resolution image and a second-resolution image of a remote sensing object;
B. comparing the high-resolution image with the low-resolution image, and screening the characteristic region;
C. extracting a feature pixel set from a feature region marked in the low resolution image;
D. deleting the feature pixel set extracted in the step C from the high-resolution image, and then extracting the rest feature pixel sets;
E. and C, carrying out enhancement processing on the feature pixel set extracted in the step C and the step D, replacing a corresponding pixel set in the high-resolution image with the feature pixel set subjected to the enhancement processing, and then carrying out anti-logarithmic transformation on the high-resolution image.
2. The remote sensing image enhancement method according to claim 1, wherein: in step B, screening the feature region includes the steps of,
b1, respectively carrying out logarithmic transformation on the high-resolution image and the low-resolution image;
b2, respectively carrying out high-pass filtering on the transformed high-resolution image and the transformed low-resolution image to obtain a high-frequency image, and respectively carrying out low-pass filtering on the transformed high-resolution image and the transformed low-resolution image to obtain a low-frequency image;
and B3, traversing the high-frequency image and the low-frequency image with high resolution, the high-frequency image and the low-frequency image with low resolution respectively, marking the areas with gray scale change ranges smaller than a set threshold value in each image, comparing the marked areas in the four images, deleting the marked areas in the four images in the high-resolution image and the low-resolution image, and taking the rest areas as characteristic areas.
3. The remote sensing image enhancement method according to claim 2, wherein: in step C, extracting a set of feature pixels in a feature region marked in the low resolution image comprises the steps of,
c1, gradually expanding the identification radius outwards by taking any gray extreme point in the characteristic region as a central dot;
and C2, after the radius is sequentially identified every time expansion, if no characteristic pixel exists in the radius identification range, calculating the deviation of the included angle between the Euler direction of the newly increased pixel point and the center dot in the radius identification range and the gray gradient direction of the newly increased pixel point and the deviation of the gray gradient amplitude of the newly increased pixel point and the gray gradient difference of the newly increased pixel point and the center origin, judging the pixel point as the characteristic pixel if the calculated included angle and the gray gradient deviation are smaller than a set threshold value, and if the characteristic pixel exists in the radius identification range, calculating the included angle between the Euler direction of the newly increased pixel point and the gray gradient direction of the newly increased pixel point in the radius identification range and the gray gradient amplitude of the newly increased pixel point and the gray gradient difference of the newly increased pixel point and the pixel point closest to the Euler distance, and judging the pixel point as the characteristic pixel if the calculated included angle and the gray gradient deviation are smaller than the set threshold value.
4. The remote sensing image enhancement method according to claim 2, wherein: in step D, extracting a set of feature pixels in a feature region marked in the high resolution image comprises the steps of,
d1, arbitrarily selecting a plurality of pixel points in the characteristic region as starting points;
d2, marking the pixel points adjacent to the starting point along the gray gradient direction of the starting point from the starting point, marking the pixel points adjacent to the starting point along the gray gradient direction of the newly marked pixel points until the pixel points to be marked are marked, ending the secondary marking process, and deleting other pixel points in a closed area surrounded by the marked pixel points;
and D3, repeating the step D2 until all the rest pixel points in the characteristic region are marked, wherein the rest pixel points are characteristic pixels.
5. The remote sensing image enhancement method according to claim 1, wherein: in step E, enhancement processing is performed on the feature pixel set by using a gamma correction method.
CN202310365917.0A 2023-04-06 2023-04-06 Remote sensing image enhancement method Pending CN116385268A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310365917.0A CN116385268A (en) 2023-04-06 2023-04-06 Remote sensing image enhancement method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310365917.0A CN116385268A (en) 2023-04-06 2023-04-06 Remote sensing image enhancement method

Publications (1)

Publication Number Publication Date
CN116385268A true CN116385268A (en) 2023-07-04

Family

ID=86974732

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310365917.0A Pending CN116385268A (en) 2023-04-06 2023-04-06 Remote sensing image enhancement method

Country Status (1)

Country Link
CN (1) CN116385268A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117611977A (en) * 2024-01-23 2024-02-27 深圳智锐通科技有限公司 Signal processing circuit in visual recognition system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117611977A (en) * 2024-01-23 2024-02-27 深圳智锐通科技有限公司 Signal processing circuit in visual recognition system
CN117611977B (en) * 2024-01-23 2024-04-30 深圳智锐通科技有限公司 Signal processing circuit in visual recognition system

Similar Documents

Publication Publication Date Title
CN107154040B (en) Tunnel lining surface image crack detection method
CN107507173B (en) No-reference definition evaluation method and system for full-slice image
CN108364280B (en) Method and equipment for automatically describing structural crack and accurately measuring width
CN110084241B (en) Automatic ammeter reading method based on image recognition
CN109785285B (en) Insulator damage detection method based on ellipse characteristic fitting
CN112419250A (en) Pavement crack digital image extraction, crack repair and crack parameter calculation method
CN107038416B (en) Pedestrian detection method based on binary image improved HOG characteristics
CN113344956B (en) Ground feature contour extraction and classification method based on unmanned aerial vehicle aerial photography three-dimensional modeling
CN109559324A (en) A kind of objective contour detection method in linear array images
CN110533679B (en) SAR image edge detection method based on logarithm transformation and gabor convolution
CN114219773B (en) Pre-screening and calibrating method for bridge crack detection data set
CN110245600B (en) Unmanned aerial vehicle road detection method for self-adaptive initial quick stroke width
CN116385268A (en) Remote sensing image enhancement method
CN114596551A (en) Vehicle-mounted forward-looking image crack detection method
CN112307803A (en) Digital geological outcrop crack extraction method and device
CN113177456A (en) Remote sensing target detection method based on single-stage full convolution network and multi-feature fusion
CN101739667B (en) Non-downsampling contourlet transformation-based method for enhancing remote sensing image road
CN110555373A (en) Concrete vibration quality real-time detection method based on image recognition
CN113610024B (en) Multi-strategy deep learning remote sensing image small target detection method
CN117036737A (en) Feature extraction and matching method based on information entropy, GMS and LC significant detection
CN117274240A (en) Bearing platform foundation concrete surface crack identification method
CN111091071A (en) Underground target detection method and system based on ground penetrating radar hyperbolic wave fitting
CN117152163B (en) Bridge construction quality visual detection method
CN117710399A (en) Crack contour extraction method in geological survey based on vision
CN116718599A (en) Apparent crack length measurement method based on three-dimensional point cloud data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination